Performance analysis of YOLOv8n object detection algorithm on RaspberryPi and NVidia Jetson Nano microcomputers

Authors

DOI:

https://doi.org/10.18372/2073-4751.77.18658

Keywords:

UAV, pattern recognition, image recognition, object detection, neural network, YOLO, RaspberryPi, Jetson Nano, autopilot, autonomous control, single board computer

Abstract

The scientific article describes approaches to the implementation of a complex of autonomous control of UAVs. In particular, the architecture with the use of a single-board computer that can be installed directly on the aircraft is highlighted. Potential single-board computers were selected for such an architecture of the complex construction and their performance was investigated. For evaluation, an experiment was described and conducted to run the pattern recognition algorithm based on the Yolo v8 nano neural network. The results of the algorithm are given in the article. Based on the conducted experiment, it was determined that it is advisable to use Jetson Nano for similar algorithms. And RaspberryPi does not have enough power for a specific task with the selected algorithm.

References

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Published

2024-04-01

How to Cite

Lukash, Y. (2024). Performance analysis of YOLOv8n object detection algorithm on RaspberryPi and NVidia Jetson Nano microcomputers. Problems of Informatization and Control, 1(77), 61–67. https://doi.org/10.18372/2073-4751.77.18658

Issue

Vol. 1 No. 77 (2024)

Section

Статті

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